Localization of indoor wheeled mobile robots (IWMR) has been an active area of research since the 1980s. Self-localization is defined as the capability to determine its own posture (orientation and location). Dead-reckoning navigation techniques, which rely on internal sensors such as the odometer or encoders, have been the earliest of techniques used in this field. In dead-reckoning systems, the posture error grows without bound, which limited its use as a secondary technique with other localization methods. Different types of external sensors or combinations of external sensors have also been used for localization. The sensors employed include video cameras, infrared sensors, laser sensors, ultrasonic sensors, radio frequency identification sensors (RFID), sonar sensors and global positioning system (GPS), etc. Different sensor-based localization methods have their own application limitations. Camera-based localization depends heavily on the lighting condition of the ambient environment, thus rendering it ineffective under insufficient lighting conditions. RFID sensor needs to know the ambient environment, henceforth reducing its effectiveness in situations where knowledge about the environment is unavailable.
Ultrasonic sensors are more sensitive to environmental noises. For example, in a noisy environment the diffusion of ultrasonic waves could be high which reduces the accuracy. Thus ultrasonic sensors cannot be used as a stand-alone sensor for localization in a noisy environment and can only be used along with other sensors. GPS-based localization has relatively low accuracy, slow update rate, and availability issues. Laser sensor based localization supersedes these sensors in that it doesn't depend on lighting nor is it sensitive to environmental noise. Also, unlike GPS based localization, laser-based IWMR localization does not need a local base station, which makes GPS-based localization an expensive technique for indoor applications.
Laser sensor based IWMR localization generally needs a priori knowledge of the entire environment or at least the landmark features in the environment. Thus existing laser sensor based localization algorithms can be divided into two categories—algorithms that search for patterns in the entire environment and algorithms that seek landmark features on the floor such as lines, line segments, and so on.
Localization results have also been used to update local portions of a global map. The process of updating a global map using the localization results is termed as Simultaneous Localization and Mapping (SLAM). Different algorithms have been proposed to perform SLAM. There have been efforts to reduce the complexity of these algorithms. If the dependence on sensor fusion techniques to obtain accurate localization results is reduced, complexity of the SLAM algorithms can also be lowered. In the past, SLAM problems have been conducted in static and dynamic environments.